Learning sparse SVM for feature selection on very high dimensional datasets

ICML 2010 - Proceedings, 27th International Conference on Machine Learning

Volumn , Issue , 2010, Pages 1047-1054

  Tan, Mingkui ^a   Wang, Li ^b   Tsang, Ivor W ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL VARIABLE; CONVEX RELAXATION; CUTTING PLANE ALGORITHMS; FEATURE SELECTION; FEATURE SELECTION METHODS; GENERALIZATION PERFORMANCE; GLOBAL CONVERGENCE; HIGH DIMENSIONAL DATASETS; HIGH-DIMENSIONAL PROBLEMS; INPUT FEATURES; MIXED-INTEGER PROGRAMMING; MULTIPLE KERNEL LEARNING; REAL-WORLD DATASETS; SPARSE REPRESENTATION;

FEATURE EXTRACTION; INTEGER PROGRAMMING; RELAXATION PROCESSES;

SUPPORT VECTOR MACHINES;

EID: 77956551904     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Blum, A. L. and Langley, P. Selection of relevant features and examples in machine learning. Artificial Intelligence, 97:245-271, 1997.
2. Bradley, P. S. and Mangasarian, O. L. Feature selection via concave minimization and support vector machines. In ICML, 1998.
3. Chan, A.B., Vasconcelos, N., and Lanckriet, G.R.G. Direct convex relaxations of sparse SVM. In ICML, 2007.
4. Chen, J. and Ye, J. Training SVM with indefinite kernels. In ICML, 2008.
5. Fung, G.M. and Mangasarian, O.L. A feature selection newton method for support vector machine classification. Computational Optimization and Applications, 28: 185-202, 2004.
6. Guyon, I. and Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res., 3:1157-1182, 2003.
7. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. Gene selection for cancer classification using support vector machines. Machine Learning, 46:389-422, 2002.
8. Hsieh, C.-J., Chang, K.-W., Lin, C.-J., Keerthi, S. S., and Sundararajan, S. A dual coordinate descent method for large-scale linear SVM. In ICML, 2008.
9. Joachims, T. Training linear SVMs in linear time. In ACM KDD, 2006.
10. Kelley, J. E. The cutting plane method for solving convex programs. Journal of the Society for Industrial and Applied Mathematics, 8(4):703-712, 1960.
11. Kim, S.-J. and Boyd, S. A minimax theorem with applications to machine learning, signal processing, and finance. SIAM Journal on Optimization, 2008.
12. Li, Y.F., Kwok, J.T., Tsang, I.W., and Zhou, Z.H. A convex method for locating regions of interest with multi-instance learning. In ECML, 2009a.
13. Li, Y.F., Tsang, I.W., Kwok, J.T., and Zhou, Z.H. Tighter and convex maximum margin clustering. In AISTATS, 2009b.
14. Ma, J., Saul, L. K., Savage, S., and Voelker, G. M. Identifying suspicious URLs: An application of large-scale online learning. In ICML, 2009.
15. Rakotomamonjy, A., F., Bach, Y, Grandvalet, and S., Canu. SimpleMKL. J. Mach. Learn. Res., 9:2491-2521, 2008.
16. Weston, J., Elisseeff, A., and Schölkopf, B. Use of the zero-norm with linear models and kernel methods. J. Mach. Learn. Res., 3:1439-1461, 2003.
17. Xu, Z., Jin, R., J., Ye, Lyu, Michael R., and I, King. Nonmonotonic feature selection. In ICML, 2009.
18. Zhu, J., Rossett, S., Hastie, T., and Tibshirani, R. 1-norm support vector machines. In NIPS, 2003.